Sentence examples for modification by covariates from inspiring English sources

Logistic regression was used to check for effect modification by covariates [mother's age and education, socioeconomic status, mother's age at first pregnancy, presence of male sibling younger than 3 (because of the perceived preference given to male children, their homes may be safer), and number of siblings].

Effect modification by covariates was evaluated by addition of interaction terms to the model; interaction terms were removed if they did not reach statistical significance.

We included interaction terms to evaluate effect modification by covariates.

Data from these biennial questionnaires were used to assess potential confounding and effect modification by covariates.

We explored possible exposure effect modification by covariates using product terms.

The modifications of ORs between univariate and multiple conditional logistic regression analysis were due to effect modification by covariates and not because of change in selection of individuals when restricting to those with complete data sets (data not shown).

First, it assumes there is no effect modification by covariate history.

Nor was there evidence of significant effect-modification by these covariates above or below the corresponding adiponectin cut points.

In the second stage, we used meta-regression approaches to combine time-series results across cites and to assess effect modification by selected ecologic covariates.

We performed subgroup analyses to examine potential effect modification by the following covariates: treatment arm (intensive lifestyle or placebo), baseline age (median cohort value, <50 or ≥50 years), sex (male or female), race/ethnicity (white or nonwhite), baseline BMI (overweight, <30 k/m, or obese, ≥30 k/m), and baseline total calcium intake (median cohort value, <921 or ≥921 mg/day).

We did not find strong evidence of effect modification by comorbidities or sociodemographic covariates.